Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms

Abstract

The acceptance rate of Electric Vehicles (EVs) in the transport industry has increased substantially due to the augmented interest towards sustainable transportation initiatives. However, their impact in terms of increased power demand on the electric power market can increase real power losses, decrease voltage profile, and consequently decrease voltage stability margins. It is necessary to install Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) at optimal locations to decrease the EV load effect in the Radial Distribution System (RDS). This paper addresses a multi-objective optimization technique to obtain simultaneous EVCS & DG placement and sizing. The problem is formulated to optimize real power losses, Average Voltage Deviation Index (AVDI), and Voltage Stability Index (VSI) of the electrical distribution system. Simulation studies were performed on the standard IEEE 33-bus and 69-bus test systems. Harries Hawk Optimization (HHO) and Teaching-Learning Based Optimization (TLBO) algorithms were selected to minimize the system objectives. The simulation outcomes reveal that the proposed approach improved system performance in all aspects. Among HHO and TLBO, HHO is reasonably successful in accomplishing the desired goals.

A Two-Level Desired Load Profile Tracking Algorithm for Electric Two-Wheeler Charging Stations

“…In more than two thirds of all charges, the initial state of charge was above 50% SOC and 11.6% of all charges were even initiated with a nearly full battery with more than 90% SOC. The required amount of energy ሺܴ ௌ ሻ of the 160 E2Ws is determined by (9). The battery capacity of an E2W is 1200W and the maximum allowable charging/discharging power is 400/-400W respectively.…”

“…According to the draft national power development plan for the period 2021-2030, with a vision to 2045 (Draft Master Plan VIII or PDP8), the total installed capacity in 2020 was about 69,094MW, with hydropower accounting for 30%, coal-fired power for 30%, gas natural/diesel power for 13%, and renewable energy for 26% [5] of total production. Total solar power generation in 2020 was 10.6×10 9 kWh, of which rooftop solar power was 1.16×10 9 kWh. In comparison with other power sources, renewable energy, especially solar power, is developed very quickly in Vietnam.…”

“…The participation of EV batteries and PV systems contributes to smart grids with lots of potential such as load shifting, valley filling, peak clipping (peak shaving), regulation and spinning reserve. Several researches on optimal allocation of EVs are mentioned in [9], combined PV and EV charging in [10] and efficiency improvement of EV's motors in [11].…”

Renewable Energy Supported Bi-directional Electric-Vehicle Charging Station Allocation in Distribution Network using INBPSO Technique

“…A GIS based optimization technique was developed in [18] for PV based EVCS location analysis and evaluation. In [19], a teaching learning-based algorithm for optimum allotment of EVCSs operating in load point mode was proposed for minimizing the node voltage errors along with line losses. One of the main drawbacks of these previous researches is the number of EVCSs to be placed optimally was fixed.…”

Allocation of EV Public Charging Station in Renewable based Distribution Network using HHO Considering Uncertainties and Traffic Congestion

“…In (Awasthi et al 2017), the optimal locations are identified in the distribution network of Allahabad city for electric vehicle charging station (EVCS) to minimize the active power loss and development cost. Real power loss is minimized in (Pal et al 2019;Ponnam and Swarnasri 2020) to CS in radial distribution network using several soft-computing techniques. In (Zhang et al 2019b), the cost for the CS is minimized with maximum service capability by the optimal allocation in Beijing city.…”